Predicted climate change may have negative impact on many environmental components including vegetation by increase of evapotranspiration and reduction of available water resources. Moreover, a growing global population and extensive use of water for irrigation and industry result in increasing demand for water. Facing these threats, quantitative and qualitative protection of water resources requires development of tools for drought assessment and prediction to support effective decision making and mitigate the impacts of droughts. Therefore, the Institute of Meteorology and Water Management, National Research Institute has developed and implemented a set of tools for the operational drought hazard assessment. The developed tools cover drought indices estimation, assessment of sensitivity to it formation and drought hazard prediction. They are streamlined into an operational scheme combined with data assimilation routines and products generation procedures. A drought hazard assessment scheme was designed to be implemented into the platform of a hydrological system supporting the operational work of hydrological forecast offices. The scheme was launched to run operationally for the selected catchments of the Odra River and the Wisla River basins. The crucial resulting products are presented on the website operated by IMWM-NRI: POSUCH@ (Operational System for Providing Drought Prediction and Characteristics) (http://posucha.imgw.pl/). The paper presents the scheme and preliminary results obtained for the drought event which began in August 2011.
Priority wise channelization of resources is the key to successful environmental management, especially when funds are limited. The study in hand has successfully developed an algorithmic criterion to compare hazardous effects of Municipal Solid Waste (MSW) dumping sites quantitatively. It is a Multi Criteria Analysis (MCA) that has made use of the scaling function to normalize the data values, Analytical Hierarchy Process (AHP) for assigning weights to input parameters showing their relevant importance, and Weighted Linear Combination (WLC) for aggregating the normalized scores. Input parameters have been divided into three classes namely Resident’s Concerns, Groundwater Vulnerability and Surface Facilities. Remote Sensing data and GIS analysis were used to prepare most of the input data. To elaborate the idea, four dumpsites have been chosen as case study, namely Old-FSD, New-FSD, Saggian and Mahmood Booti. The comparison has been made first at class levels and then class scores have been aggregated into environmental normalized index for environmental impact ranking. The hierarchy of goodness found for the selected sites is New-FSD > Old-FSD > Mahmood Booti > Saggian with comparative scores of goodness to environment as 36.67, 28.43, 21.26 and 13.63 respectively. Flexibility of proposed model to adjust any number of classes and parameters in one class will be very helpful for developing world where availability of data is the biggest hurdle in research based environmental sustainability planning. The model can be run even without purchasing satellite data and GIS software, with little inaccuracy, using imagery and measurement tools provided by Google Earth.